Biology subjects

Liu, J.

Publications and source records attributed to Liu, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Gene dosage effect and reduced core fucosylation in B lymphocytes of FUT8 heterozygous mice

Core fucosylation (alpha1,6-fucosylation) of N-glycans, catalyzed solely by fucosyltransferase 8 (FUT8), plays important roles in development, immunity, and antibody effector functions. Here we report the generation of Fut8 floxed mice using a CRISPR/Cas9-based Extreme Genome Editing (EGE) system, in which loxP sites flank exon 8 of the Fut8 gene and a LoxP-STOP-LoxP-RFP cassette was knocked in downstream of the promoter, rendering the targeted allele functionally null before Cre-mediated recombination. Crossing floxed mice with CMV-Cre transgenic mice yielded complete knockout mice carrying the Cre-recombined allele. A PCR-based genotyping method was established that unambiguously distinguishes the wild-type, heterozygous, and homozygous genotypes (+/+, delta/+, and delta/delta). Intercrossing of heterozygous mice revealed that homozygous knockout mice die during the early postnatal period, consistent with previous reports, whereas heterozygous mice are viable. Flow cytometric analysis of AAL binding to splenic B cells and AAL lectin blotting both demonstrated markedly reduced core fucosylation in heterozygous mice (60.0 +/- 6.8% and approximately 48% of wild-type levels, respectively). These results show that disruption of a single Fut8 allele substantially reduces core fucosylation, and the mouse model described here provides a practical tool for functional studies of core fucosylation.

immunology↗

bioRxiv2026

Ultrafast and Accurate Selection of High-Quality Protein Complex Models from Large-Scale Prediction

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

bioinformatics↗

bioRxiv2026

Cell type-resolved connectomic reconstruction of the spinal dorsal horn reveals sensory neuron synaptic organization and modality-specific inhibition

Touch, temperature, chemical, and noxious stimuli are detected by more than a dozen peripheral sensory neuron subtypes that together mediate somatosensation. Somatosensory neurons form synapses in the spinal cord dorsal horn, the first site of central somatosensory processing, to engage the downstream circuitry that drives both spinal reflexes and ascending pathways for perception. Yet how sensory neuron synapses are organized into the spinal cord circuitry remains poorly understood. Here we present two connectomic tools for studying somatosensory circuits of the spinal cord dorsal horn: dSC1, a large-scale, synapse-resolved electron microscopy (EM) dataset of the mouse dorsal horn, and SPINE (SPinal cord Integrated NEuron classifier), a deep-learning pipeline that identifies major sensory neuron subtypes directly from EM images. Applying SPINE to the dSC1 dataset, we found marked heterogeneity in synaptic architecture across physiologically distinct sensory neuron subtypes: low-threshold mechanoreceptors form substantially more synapses and receive far more GABAergic presynaptic inhibition than high-threshold mechanoreceptors, thermoreceptors and nociceptors. Cell-type-resolved circuit reconstruction further identified five populations of spinal inhibitory neurons (IhNs) that form synapses onto sensory neuron axons, each preferentially driven by and presynaptically inhibiting a single sensory subtype - a homotypic, modality-specific inhibitory motif. Most of these IhNs also feedforwardly inhibit other sensory pathways - a heterotypic, cross modality motif. Together, dSC1 and SPINE enable cell-type-resolved spinal cord circuit reconstruction and reveal a dual-mode logic of sensory-evoked inhibition that shapes modality-specific signaling at the earliest stage of somatosensory processing.

neuroscience↗

bioRxiv2026

A synaptic wiring diagram underlying modality-specific ascending pathways of the superficial spinal dorsal horn

The spinal cord dorsal horn transforms tactile, thermal, pruritic, and nociceptive signals from peripheral somatosensory neurons into outputs to the brain, but how dorsal horn circuits organize diverse inputs into modality-selective ascending pathways is poorly understood. Using dSC1, a large-scale electron microscopy dataset of the mouse dorsal horn, we mapped the circuitry connecting primary sensory afferents to projection neurons of the anterolateral tract (ALT), a principal pathway conveying somatosensory information to the brain. We find that afferents encoding distinct modalities are routed onto connectivity-defined populations of ALT projection neurons. Nociceptors and cold thermoreceptors directly innervate distinct lamina I ALT projection neuron populations specialized for conveying noxious and cooling signals, respectively, whereas tactile and pruritic signals instead pass through a recurrent, multilayered network involving interneurons called vertical cells before reaching a separate ALT projection population. This distinction, direct versus polysynaptic wiring, underlies marked differences in sensory convergence and establishes segregated ascending channels for conveying signals encoding the principal somatosensory modalities. Together, our findings define an architectural logic by which the dorsal horn integrates and routes tactile, thermal, pruritic, and nociceptive information into distinct ALT output pathways to the brain.

neuroscience↗

bioRxiv2026

Early dynamics of a neutrophil interferon program drive immunotherapy resistance and limit durable anti-tumor immunity

Despite the remarkable success of immune checkpoint blockade (ICB), durable clinical benefit remains limited to a subset of patients, highlighting the need for predictive biomarkers and new therapeutic targets. Here, we investigated the early immune dynamics associated with anti-PD1 responsiveness using longitudinal single-cell transcriptomics, spatial transcriptomics, and neutrophil-specific genetic perturbations in preclinical models of triple-negative breast cancer. We identified a conserved neutrophil interferon-stimulated gene (ISG) program that emerges before overt tumor divergence and predicts resistance to anti-PD1 therapy. Successful treatment was associated with an early reduction of IFN-responsive neutrophils in both blood and tumor tissues, whereas persistent neutrophil IFN signaling correlated with therapeutic resistance. Mechanistically, genetic disruption of either type I or type II interferon signaling in neutrophils enhanced anti-PD1 efficacy and promoted durable anti-tumor immune memory. Spatial analyses further revealed an inverse association between IFN-responsive neutrophils and tertiary lymphoid structure-enriched regions during effective therapy. Together, these findings identify IFN-responsive neutrophils as early determinants of immunotherapy outcome and establish dynamic neutrophil interferon programs as candidate blood-based biomarkers and therapeutic targets for cancer immunotherapy.

cancer biology↗

bioRxiv2026

Evaluating Clustering and Imputation Approaches for Multivariate Longitudinal Clinical Data Using a Real-World eCDF-Copula Resampling Framework

The rapid growth of technologies such as wearable devices and high-throughput sequencing has generated unprecedented volumes of longitudinal data, often with multiple correlated features. This trend highlights the need for robust clustering methods tailored to multi-feature longitudinal datasets. Although previous reviews have benchmarked clustering methods in this context, most evaluations relied heavily on simulated and overly simplified datasets, which substantially limit their interpretability and applicability to real-world data. Missing data is also ubiquitous in longitudinal data, yet understudied in the context of clustering. To address these gaps, we systematically evaluate clustering methods across three distinct types of real-world longitudinal datasets: (1) a high-dimensional, low-timepoint metagenomic study, (2) a low-dimensional, high-timepoint multi-site repeated-measure study, and (3) a high-dimensional, high-timepoint repeated-measure study. We further investigate how missing data and different imputation strategies influence clustering performance. In addition, we introduce a novel resampling framework based on the empirical cumulative distribution function (eCDF) and a copula. This framework enables us to simulate multi-feature longitudinal data that preserves the correlation structure of real datasets, while also allowing for controlled variations such as sample size. Our simulation framework naturally accommodates missing data, thereby providing a flexible tool for benchmarking and comparing clustering methods in more realistic longitudinal settings.

bioinformatics↗

bioRxiv2026

Inferring cascade drivers of VEXAS syndrome by a causal machine learning tool CauNagi

VEXAS syndrome is an adult-onset severe autoinflammatory disease caused by somatic mutations in UBA1, yet the cascade mechanisms linking primitive hematopoietic abnormalities to mature myeloid dysfunctions remain largely unknown. Identifying master regulators of a progressive disease, a black-box process, from complex transcriptomic data also remains challenging. To address this challenge, we developed CauNagi, a computational framework for prioritizing cascade candidate regulators (CCRs). CauNagi integrates a causal representation learning module derived from CausCell with an iterative deep learning backbone adapted from UNAGI; in addition, CauNagi extends these two components with a unique downstream module for CCRs analysis designed to characterize regulatory propagation across hierarchical cellular states. Mechanistically, CauNagi iteratively integrates causal disentangled representation learning with (1) disease-stage cell-state trajectory reconstruction and (2) dynamic regulatory analysis. Benchmarking on single-cell transcriptomic datasets showed that CauNagi preserved cell-type structure in idiopathic pulmonary fibrosis (IPF) and enriched known acute myeloid leukemia(AML)-associated genes among its top-ranked global regulators. When applied to VEXAS syndrome, CauNagi readily revealed inflammatory responses, endoplasmic reticulum stress, and myeloid bias, consistent with the disease features. Furthermore, the CCRs analysis module of CauNagi assisted us in identifying 36 causal drivers, with SPI1, NFKB1, STAT3, and FOS prioritized as high-confidence regulatory hubs linking aberrant myeloid differentiation and inflammatory programs. These findings were further supported by an independent single-cell transcriptomic dataset from a murine VEXAS model. Overall, CauNagi provides a computationally efficient and systematic framework for identifying candidate causal regulators. Beyond hematopoietic diseases, CauNagi may also be applicable to other progressive disorders for which multistage single-cell transcriptomic datasets are available. CauNagi is available at https://github.com/steamed-stuffed-bun/CauNagi.

bioinformatics↗

bioRxiv2026

ctDNA Release Heterogeneity in Luminal Breast Cancer Revealed by Single-Cell Multi-Omics Spatial Analysis

Backgroud Breast cancer is a leading cause of cancer-related mortality, and HR+/HER2-; is the predominant subtype. Molecular pathology, including PIK3CA and ESR1 mutation, informs prognosis and treatment selection. Circulating tumor DNA (ctDNA) offers a non-invasive approach for cancer monitoring, but its clinical utility is limited by tissue plasma discordance, reflecting heterogeneous ctDNA release. The cellular and spatial determinants of this release remain poorly understood, particularly in HR+/HER2-; subtype, where heterogeneity across subclones complicates liquid biopsy interpretation. Results To identify the cellular and spatial determinants of ctDNA release, we developed scMASTER, integrating single-cell transcriptomes and mutations, spatial transcriptomics and matched tumor and plasma DNA sequencing. In ten patients with non-metastatic HR+/HER2- breast cancer, eight recurrent malignant epithelial states showed distinct ctDNA release potential. Antigen-presentation, stress, mitosis and mesenchymal-like states showed high release. PIK3CA and ESR1 mutations were linked to distinct functional states and altered ctDNA detectability. Three spatial architectures, EMT-enriched regions, intraductal luminal progenitors and avascular hypoxic zones, showed high release. Tumor-microenvironment interactions were bidirectional: anti-tumoral immunity correlated with reduced release, whereas immunomodulatory components correlated with increased release. A stratification model based on these features identified high-risk, immunosuppressed patients with poor prognosis, validated across external cohorts. Conclusions Our study establishes a single-cell spatial framework for decoding ctDNA release heterogeneity, linking tumor states, somatic mutations, spatial architecture, and immune interactions to ctDNA release. These findings provide mechanistic insights into tissue-plasma discordance, offer a biological rationale for interpreting ctDNA dynamics, refine patient risk stratification and liquid biopsy-based monitoring in HR+/HER2-; breast cancer.

cancer biology↗

bioRxiv2026

WHAT IS NORMAL? MULTIMODAL CHARACTERIZATION OF NON-DISEASED PEDIATRIC DUODENAL BIOPSIES USING MACHINE LEARNING IMAGE ANALYSIS AND TRANSCRIPTOMICS

Pediatric endoscopy is performed only when clinically indicated, limiting access to healthy duodenal tissue. Biopsies with duodenal no pathologic abnormality (NPA) are often used as controls despite the presence of symptoms or inflammatory disease found elsewhere in the gastrointestinal (GI) tract. We characterized pediatric duodenal NPA tissue across clinical, histologic, cellular, and transcriptomic domains. Methods Archival duodenal NPA biopsies were obtained with clinical metadata and hematoxylin-and-eosin whole-slide images (WSIs). Duodenal mRNA-seq data were analyzed from a subset of patients with duodenal NPA. Clinical metadata and WSIs underwent machine-learning analysis, cell populations were quantified from WSIs, and RNA-seq data underwent differential expression and pathway-enrichment analyses. Results The primary cohort included 195 patients with duodenal NPA. Comparisons between patients with non-duodenal GI disease and those with no GI disease showed differences in inflammatory biomarkers and follow-up utilization. Unsupervised clinical clustering identified three clusters with partial enrichment for IBD with colonic inflammation and Eosinophilic Esophagitis (EoE) with esophageal inflammation. Supervised clinical classification showed modest discrimination. WSI clustering showed limited disease-status discrimination, and cell quantification showed no significant group differences. In the separate RNA-seq cohort of 43 patients, differential-expression and pathway-enrichment analyses identified transcriptional and pathway-level differences between disease-status groups. Conclusions This multi-level characterization indicates that pediatric duodenal NPA tissue should not be treated as a uniform control category. Clinical metadata and transcriptomics revealed clinical and molecular heterogeneity, while histologic and cell analyses showed limited disease-status separation, supporting a refined definition of control tissue.

physiology↗

bioRxiv2026

Histotripsy-induced vascular remodeling in the tumor microenvironment enhances local and off-target intratumoral drug delivery

Solid tumors are known to be refractory to treatment due to a poorly perfused microenvironment that blocks immune cell infiltration and effective drug delivery. Histotripsy is a non-invasive focused ultrasound modality that generates cavitation microbubbles to mechanically disrupt solid tumors, resulting in decreased hypoxia and stimulation of tumor-targeted adaptive immune responses. In this study, we aim to investigate whether post-histotripsy hypoxia reduction and cytotoxic immune cell infiltration are associated with normalization of tumor vasculature and enhanced perfusion. Structural analysis of both treated and contralateral off-target tumors following partial histotripsy revealed significant vascular remodeling, enhanced vascular integrity, and decreased vascular leakage indicative of vascular normalization in multiple in vivo models of melanoma, hepatocellular carcinoma, and pancreatic adenocarcinoma. These effects appeared antigen-specific, as no remodeling was observed in contralateral off-target tumors of discordant pathology. Mechanistically, CXCR3+CD8+ T cells played a role in mediating these vascular changes, as genetic knockout of CD8 and pharmacological antagonism of CXCR3 impaired both remodeling and normalization. Additionally, significant changes in endothelial CXCR4 and Angiopoietin-1 and -2 expressions were observed following histotripsy, indicating their potential involvement in promoting vascular normalization and remodeling. Significant vascular remodeling was accompanied by enhanced tumor perfusion and intratumoral delivery of chemotherapeutic agents and therapeutic monoclonal antibodies. These findings suggest that histotripsy not only creates a more favorable microenvironment for immunotherapy, but also optimizes drug delivery into solid tumors. Combinatorial approaches using histotripsy with chemotherapy or immunotherapy may be a novel therapeutic strategy to overcome current limitations of conventional solid tumor therapies.

immunology↗

bioRxiv2026

ARSA: an autonomous research scientist for target nomination in Alzheimer's disease and related dementias

Expanding therapeutic options for Alzheimer's disease and related dementias (ADRD) requires biologically grounded targets, yet nomination demands labor-intensive analysis and multidisciplinary evidence synthesis. To address this challenge, we present ARSA, an autonomous research scientist that transforms natural-language research interests and molecular data into prioritized, evidence-grounded target shortlists. ARSA formulates and audits hypotheses, adapts molecular analyses to observed results, and prioritizes targets using cross-cohort evidence and disease-specific knowledge, preserving the evidence and decisions underlying each nomination. Across three complementary evaluations, we show that ARSA generates hypotheses corresponding to subsequent research and identifies credible candidates within and beyond community nomination records. In structured assessment by 14 experts spanning all four technical cores of the Indiana University School of Medicine-Purdue University TREAT-AD Center, every expert assigned higher mean credibility to ARSA-retained candidates than to rejected comparators. ARSA enables systematic, transparent target exploration, opening opportunities to broaden the therapeutic mechanisms investigated in ADRD.

bioinformatics↗

bioRxiv2026

Neuropeptides NLP-10 and FLP-18 stimulate lipid catabolism in chronically diet-restricted Caenorhabditis elegans

Neuropeptides mediate inter-tissue communication in energy metabolism, yet most remain functionally uncharacterized. Using C. elegans, we uncover a neuropeptide signaling cascade that drives lipid catabolism under chronic dietary restriction. We show that impaired pharyngeal function leads to dyspepsia and nutrient insufficiency, which activates a signaling axis comprising two peptide-receptor pairs, NLP-10-NPR-35 and FLP-18-NPR-4. This cascade originates in ADL sensory neurons, relays through multiple interneurons, and enhances triglyceride hydrolysis in the intestine. Genetic disruption of any component of this axis restores fat accumulation in diet-restricted mutants, whereas overexpression of individual components in well-fed animals is insufficient to drive fat mobilization. Importantly, the same neuropeptide axis is engaged in starved wild-type animals, indicating a general response toward energy deficit rather than mutant-specific effects. These findings reveal a conserved neuroendocrine circuit that senses internal energy shortage and orchestrates systemic metabolic adaptation, providing a conceptual framework for how neuropeptide networks integrate nutritional status with energy balance in animals.

neuroscience↗

bioRxiv2026

PROXINAUT: An Automation Platform for Rapid Hit-to-Degrader Discovery

Massive library-based platforms enable rapid early hit discovery, yet subsequent hit resynthesis and validation remain major bottlenecks in drug discovery. This delay is amplified when optimizing hits, or elaborating structures towards more advanced modalities, such as chemical inducers of proximity (CIPs). Here, we report PROXINAUT, an end-to-end workflow integrating self-encoded library-based hit discovery with an automated parallel solid-phase synthesis platform to streamline hit discovery, validation, optimization, and degrader development. As a demonstration, screening a 176k-member benzimidazole library against BRD4 yielded six hit candidates, which were obtained via parallel automated synthesis and validated as submicromolar binders. The automation workflow empowered a rapid structure activity relationship exploration around the whole scaffold and resulted in a variant with six fold improved binding affinity. Next, we aimed to automatize the development of bifunctional degraders. Solid phase synthesis typically leaves a C-terminal amide as a synthetic artifact that can compromise druglike properties. We developed a strategy to repurpose this resin-attachment site, converting it into functional degrons featuring either FBXO31-targeting C-terminal amides or cereblon-binding cyclic imides. Operating without human intervention, our automated platform executes up to 16 synthetic steps across multiple parallel structures, enabling true de novo synthesis of full bifunctional scaffolds, where ligand, linker and E3 recruiter variations can all be explored within the same workflow. This resulted in cell-active BRD4 degraders with subnanomolar to nanomolar potency. Overall, we show how library selections can merge with multi-step parallel automation to convert hits directly into validated leads and advanced degrader modalities.

bioengineering↗

bioRxiv2026

Brain Mechanisms of Lexicality in Bilingual Emotion Processing

Understanding how native speakers and second language (L2) learners process language differently is fundamental to theories of language acquisition, comprehension, and use. A central debate in this literature concerns the inconsistent findings in emotional prosody processing between native speakers and L2 learners, partly attributable to stimulus lexicality: some studies use real words, whereas others use pseudowords. To resolve this discrepancy, we measured fMRI responses in 15 native speakers and 15 L2 learners while they listened to Mandarin real-word and pseudoword stimuli. Both groups engaged in overlapping auditory, cognitive, and affective regions, including the auditory cortex, inferior frontal cortex, superior temporal cortex, and the temporoparietal junction. However, the two groups showed distinct responses depending on lexicality: native speakers showed stronger recruitment of temporal regions for pseudowords, whereas L2 learners showed greater frontoparietal activation for real words. These patterns were consistent across positive and negative prosody conditions. Furthermore, among L2 learners, higher proficiency was associated with greater frontal activation for real words, but not pseudowords. Together, these findings demonstrate that the differences between native speakers and L2 learners depend critically on stimulus lexicality, highlighting the role of lexicality in bilingual emotion processing.

neuroscience↗

bioRxiv2026

Identification of co-expressed gene sets for major molecular initiating events in rodent liver carcinogenesis using wild type and knock out rats

Liver tumors are the most common carcinogenic outcome observed in two-year bioassays used for regulatory risk assessment of chemicals and pharmaceuticals. Because these traditional long-term studies are resource-intensive and not feasible for most chemicals, there is a critical need to develop alternative approaches that can identify potential rodent liver carcinogens in short-term studies (e.g., [≤] 1 month). Biomarkers associated with molecular initiating events (MIEs) of early rodent liver tumorigenesis may enable the prediction of two-year outcomes from such studies. Here, we identified transcriptional changes associated with six key initiators of rat liver tumorigenesis: five transcription factors (TFs) associated with non-genotoxic modes of action (AhR, PXR, CAR, PPAR, and ER), and tumor suppressor protein p53, which is activated by diverse cellular stressors including genotoxicity. Three known chemical activators for each TF were administered orally once daily for five days at doses associated with tumorigenic responses in previous rat cancer bioassays. RNA-seq identified differentially expressed genes (DEGs) that were consistently regulated by at least two activators in wild-type but not in corresponding TF knockout animals. Gene set enrichment analysis of ranked differential expression results demonstrated enrichment of pathways reflecting established TF biology. When comparing the consensus DEGs for each TF to a library of chemically induced, liver-specific gene expression profiles, we found that in most cases the chemicals with the most similar profiles to the consensus lists were known to activate the corresponding TF. Collectively, these findings identified candidate gene sets that may have utility for monitoring the induction of carcinogenesis-associated MIEs in short-term studies.

pharmacology and toxicology↗

bioRxiv2026

Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

neuroscience↗

bioRxiv2026

Localized SARS-CoV-2 Infection Triggers a Tissue-Wide Antiviral Response and Functional Impairment of Olfactory Sensory Neurons

Olfactory dysfunction is a hallmark of COVID-19, yet the mechanisms by which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes widespread sensory impairment remain incompletely understood. We compared the olfactory epithelial tropism of multiple SARS-CoV-2 variants in a mouse model and observed that Alpha and Beta variants exhibited the greatest infectivity for the olfactory epithelium (OE), whereas Omicron rarely infected this tissue despite comparable pulmonary viral titers. We further defined the effects of SARS-CoV-2 on olfactory sensory neurons (OSNs) using immunohistochemistry, single-cell RNA sequencing, spatial gene expression characterization, and functional odor stimulation. The localized infection of sustentacular cells triggered a tissue-wide interferon-stimulated antiviral response that extended beyond infected regions for Alpha and Beta but was largely absent following Omicron infection. Mature OSNs transiently adopted an interferon-responsive state before exhibiting persistent downregulation of odorant signal transduction, mitochondrial, and activity-dependent gene pathways. Consistent with these transcriptional changes, odor stimulation failed to elicit normal activity-dependent gene expression during SARS-CoV-2 infection, indicating impaired neuronal function without widespread neuronal loss. Progressive accumulation of macrophages further indicated sustained inflammatory remodeling of the OE. Together, these findings demonstrate that SARS-CoV-2 infection initiates tissue-wide antiviral signaling in the OE that persistently disrupts OSN function, providing a mechanistic framework for COVID-19-associated anosmia.

neuroscience↗

bioRxiv2026

An atlas of transcription factor cooperation reveals how motif readers shape regulatory output

Regulatory motifs are conventionally associated with named transcription factors (TFs), yet a motif label need not identify the protein that reads the sequence or the regulatory consequence that follows in a given cell. We analyzed 1,552 TF binding datasets in 10 cell types using ARES, a multi-agent system that tests competing mechanisms of TF-motif dependencies in a specific cellular context against multi-omic data. We found that the inferred mechanisms converged on three operating routes: direct sequence recognition, protein-mediated recruitment or exclusion, and regulatory context. Importantly, the predictive motifs of the target TF binding were read by their conventionally "canonical" TFs in only one third of resolved dependencies, and these "canonical" TFs were expressed much less often than the inferred readers. Furthermore, we observed that motif similarity was associated with shared regulatory region type but not shared transcriptional outcome, whereas reader identity was associated with both and the only feature among the examined associated with outcome. In validation case studies where an inferred reader was perturbed, target TF occupancy fell in proportion to reader binding before perturbation, and a natural variant disrupting the predictive motif altered target TF binding at every intermediate step of the inferred mechanism. These observations were further supported by single-cell perturbation, in vitro cooperativity and evolutionary constraint. Together, these results separate motif identity from reader identity and regulatory output, suggesting that a motif acts as an address whose regulatory consequence is shaped in trans by the protein that interprets it.

genomics↗